Cystic fibrosis (CF) is a recessive genetic disorder, but there is great variability in pulmonary disease severity and survival, even among patients who are homozygous for the most prevalent mutation, F508del. For lung function, twin and sib studies have shown that >50% of the variability can be accounted for by non- CFTR genetic influence. We hypothesize that the identification of genetic modifiers of CF lung disease will help to clarify disease pathogenesis and suggest therapeutic targets, as soon as we can define links between genetic variation and disease mechanism. A recent genome-wide association study (GWAS1) in 3,467 CF patients by the North American CF Gene Modifier Consortium, in which UNC takes a lead role, offers robust support for this hypothesis. In GWAS1, our strongest association (p=1.2 x 10-9) localized to an intergenic region of chromosome 11p13, near APIP, a regulator of apoptosis and EHF, an epithelial transcription factor. Another robust association (p=9 x 10-8) was discovered at chr6p21.3, in the HLA class II region, and has strong biological plausibility as a modifier for CF airways disease (infection/inflammation). The GWAS1 study further revealed 5 suggestive loci (p<1.75 x 10-6), which all contain worthy candidates for follow-up. Ongoing studies are adding considerably to the interpretation of the GWAS1 data; for example, we now have extensive gene expression data from ~800 samples of lymphoblast cell lines (LCLs) from GWAS1 patients, and these are already informing our interpretation of the genetic variation near EHF/APIP. The Consortium has recently expanded to include a large French population, and funding has been procured from the U.S. CF Foundation and French genome center for GWAS2 in an additional 3,500 CF patients, which will add considerable power. All of these efforts make this the opportune time to fully analyze and integrate all the GWAS data in ~7,000 CF patients, plus the complementary data (expression; targeted sequencing; and exome sequencing), in a comprehensive fashion. Other statistical applications, such as meta-analysis, eQTLs, and pathway analyses will be fully exploited to identify and validate the multiple loci. Additional available phenotypes and genotypes (in CF and other populations, such as COPD, asthma) will also studied to add biological relevance. These integrated analytical studies will identify and validate key loci, and allow planning for follow-up biological investigations, which will be undertaken to define the link between genetic variation and disease mechanism.